Methods and systems for providing purchase recommendations based on responses to inquiries on product attributes

ABSTRACT

Systems and methods are disclosed for providing purchase recommendations. According to some examples, a method may include: determining respective frequency distributions of a plurality of vehicle attributes, the frequency distributions being determined based on occurrences of values of the plurality of vehicle attributes in a set of vehicles; selecting a vehicle attribute from the plurality of vehicle attributes based on the frequency distributions of the plurality of vehicle attributes; transmitting, to a user device, an inquiry for user preference regarding the selected vehicle attribute; receiving, from the user device, a response indicating the user preference; and presenting, to the user device, a recommendation of one or more vehicles determined based on the received response.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This patent application is a continuation of U.S. patent applicationSer. No. 16/506,114, filed on Jul. 9, 2019, issued as U.S. Pat. No.10,482,523 B1, the entirety of which is incorporated herein byreference.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally tomethods and systems for providing product recommendations to users and,more particularly, to artificial intelligence-based methods and systemsfor providing vehicle recommendations to users.

BACKGROUND

When shopping online, consumers may be presented with large lists ofproducts to choose from. For example, when shopping for a vehicle at awebsite of an automobile dealer or an aggregator of inventory listings,a consumer may need to sort through large inventory lists to findvehicles with a desired set of characteristics. Furthermore, differentwebsites may vary in the manner in which product inventories arepresented. Therefore, a consumer may find certain online shoppinginterfaces to be difficult or cumbersome to use.

The present disclosure is directed to addressing one or more of theseabove-referenced challenges. The background description provided hereinis for the purpose of generally presenting the context of thedisclosure. Unless otherwise indicated herein, the materials describedin this section are not prior art to the claims in this application andare not admitted to be prior art, or suggestions of the prior art, byinclusion in this section.

SUMMARY

According to certain aspects of the disclosure, systems and methods aredisclosed for providing product recommendations.

For instance, a method may include: determining respective frequencydistributions of a plurality of vehicle attributes, the frequencydistributions being determined based on occurrences of values of theplurality of vehicle attributes in a set of vehicles; selecting avehicle attribute from the plurality of vehicle attributes based on thefrequency distributions of the plurality of vehicle attributes;transmitting, to a user device, an inquiry for user preference regardingthe selected vehicle attribute; receiving, from the user device, aresponse indicating the user preference; and presenting, to the userdevice, a recommendation of one or more vehicles determined based on thereceived response.

Furthermore, a computer system may include a memory storing instructionsand one or more processors configured to execute the instructions toperform operations. The operations may include: determining respectiveestimates of normalized entropy of a plurality of vehicle attributes ofvehicles in a set of vehicles; selecting a vehicle attribute from theplurality of vehicle attributes based on the estimates of normalizedentropy; transmitting, to a user device, an inquiry for user preferenceregarding the selected vehicle attribute; receiving, from the userdevice, a response indicating the user preference; and presenting, tothe user device, a vehicle recommendation determined based on thereceived response.

Furthermore a computer system, for implementing anartificial-intelligence interactive agent for determining vehiclerecommendations based on user interaction, may include a memory storinginstructions and one or more processors configured to execute theinstructions to perform operations. The operations may include:determining respective frequency distributions of a plurality of vehicleattributes describing vehicles in a set of vehicles, the frequencydistributions being determined based on occurrences of values of theplurality of vehicle attributes in the set of vehicles; determiningrespective estimates of normalized entropy of the plurality of vehicleattributes based on the respective frequency distributions; selecting avehicle attribute from the plurality of vehicle attributes based on therespective estimates of normalized entropy; transmitting, to a userdevice, an inquiry for user preference regarding the selected vehicleattribute; receiving, from the user device, a response indicating theuser preference; and transmitting, to the user device, a web pageincluding a list of one or more vehicles from the candidate set, thelist having filters set in accordance with the user preference.

According to additional aspects of the disclosure, a non-transitorycomputer-readable medium stores instructions that, when executed by oneor more processors, cause the one or more processors to perform theaforementioned computer-implemented method or the operations that theaforementioned computer systems are configured to perform.

Additional aspects and examples will be set forth in part in thedescription that follows, and in part will be apparent from thedescription, or may be learned by practice of the disclosed embodiments.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts an exemplary system infrastructure in which a computersystem for providing vehicle purchase recommendations may beimplemented, according to one or more embodiments.

FIG. 2 depicts a method for providing vehicle recommendations to users,according to one or more embodiments.

FIG. 3 depicts a method for selecting a vehicle attribute, according toone or more embodiments.

FIGS. 4A-4H illustrate examples of normalized frequency distributionsand normalized entropy for various vehicle attributes represented in aset of vehicles, according to according to one or more embodiments.

FIGS. 5A-5B depict examples of chatbot sessions, according to one ormore embodiments.

FIG. 6 depicts an example of an interface for presenting one or morevehicle recommendations, according to one or more embodiments.

FIG. 7 depicts an example of a computing device, according to one ormore embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection. Both the foregoing general description and the followingdetailed description are exemplary and explanatory only and are notrestrictive of the features, as claimed.

In this disclosure, the term “based on” means “based at least in parton.” The singular forms “a,” “an,” and “the” include plural referentsunless the context dictates otherwise. The term “exemplary” is used inthe sense of “example” rather than “ideal.” The terms “comprises,”“comprising,” “includes,” “including,” or other variations thereof, areintended to cover a non-exclusive inclusion such that a process, method,or product that comprises a list of elements does not necessarilyinclude only those elements, but may include other elements notexpressly listed or inherent to such a process, method, article, orapparatus. Relative terms, such as, “substantially” and “generally,” areused to indicate a possible variation of ±10% of a stated or understoodvalue.

In the following description, embodiments will be described withreference to the accompanying drawings. Various embodiments of thepresent disclosure relate generally to methods and systems for providingproduct recommendations.

As will be discussed in more detailed below, in certain embodiments, aninteractive feature, such as a chatbot, may ask a user a series ofquestions, each pertaining to user preference in regard to a certainproduct attribute. In the context of automotive retailing, the productattribute may be a vehicle attribute, such as body style, exteriorcolor, or price. If the question pertains to body style, for example,the user may reply by indicating the type of body style (e.g., sedan,coupe, SUV, etc.) he or she desires. By asking such questions, itbecomes possible to narrow down a candidate set of vehicles based on theuser's response to the questions. Once the candidate set of vehicles hasbeen narrowed down, a vehicle recommendation may be made based on thenarrowed-down set of vehicles.

In order to construct a series of questions that is efficient ineliciting the most amount of information from the user using the fewestnumber of questions, the topics of the questions (e.g., the vehicleattribute, for the automotive retailing context) should be selected by amethodology that that tends to maximize efficiency. In some embodiments,normalized entropy is respectively estimated for a plurality of vehicleattributes, and a particular vehicle attribute having a high estimatednormalized entropy is selected from the plurality of vehicle attributes.By selecting a vehicle attribute having a high estimated normalizedentropy and asking a question regarding such a vehicle attribute, auser's response to the question would likely narrow down the candidateset of vehicles more so than if the question had pertained to a vehicleattribute having a low estimated normalized entropy. When presented withthe question, the user may respond with a selection of one or morevalues of the attribute (e.g., one or more types of body style).

FIG. 1 shows an environment in which a computer system for providingvehicle purchase recommendations may be implemented. The environment mayinclude a computer system 110 connected to a plurality of user devices120, such as user devices 121, 122, and 123, over a network 130. Thecomputer system 110 may also be connected to computer systems of one ormore vendors 140 through network 130. The network 130 may be a publicnetwork (e.g., the internet), a private network (e.g., a network withinan organization), or a combination of public and private networks. Userdevices 120 may each be a computer system, such as a laptop or desktopcomputer, or a mobile computing device such as a smartphone or tablet.

The computer system 110 may have one or more processors configured toperform various processes related to determining one or more productrecommendations for a user, as will be discussed in more detail below.In some embodiments, computer system 110 may be a server system, inwhich case the user devices 120 may be referred to as client devicesthat use a service provided by the computer system 110. In suchexamples, user devices 120 may interact with the computer system 110through an application installed on the user devices 120, such as a webbrowser, a web browser extension, or a mobile app. In some examples, thecomputer system 110 may have a cloud computing platform with scalableresources for computations and/or data storage, and may run one or moreapplications on the cloud computing platform to perform variouscomputer-implementable processes described in this disclosure.

The one or more vendors 140 may each be a party that sells, leases, orotherwise supplies products to users of the user devices 120. Thecomputer system 110 may have access to inventory data 141 provided bythe one or more vendors 140. Inventory data 141 may include inventorylists of products offered for sale or sold by the one or more vendors140. In some examples, the computer system 110 may aggregate inventorylists of multiple vendors 140 into an aggregated inventory list that isstored in database 112 of the computer system 110. The aggregatedinventory list may include information pertaining to the productsoffered for sale or stocked by the vendors 140. Such information mayinclude values of one or more attributes of the products.

The environment shown in FIG. 1 may be applied to the automotiveretailing industry. In the automotive retailing industry, the productsdescribed above may be vehicles and the vendors 140 may include anyparty that sells or supplies such vehicles, such as car dealers. Theusers of the user devices 120 may include consumers such as individualpersons seeking to buy automobiles. In some examples, computer system110 may be operated by a party that assists users in purchasing avehicle. For example, the computer system 110 may be operated by alender that offers car loans to consumers and desires to assistborrowers in finding a vehicle. However, in general, the computer system110 may be operated by any party that stores a list of products. In someexamples, the computer system 110 may be operated by a vendor 140, inwhich case the list of products stored in database 112 may include onlyproducts sold by the vendor 140 operating the computer system 110.

The computer system 110 may collect inventory data 141 includinginformation describing vehicle inventories of one or more vendors 140,and store the information in database 112. In the context of theautomotive retailing industry, such information may include values ofone or more vehicle attributes. Examples of vehicle attributes includemake, model, body style, exterior color, interior color, number ofdoors, the vehicle condition, engine type, transmission type, fuel type,drive type, mileage, price, year, certain features (e.g., type of seats,availability of backup camera, etc.), and fuel economy (e.g., a highwaymiles-per-gallon rating, a city miles-per-gallon rating, or a combinedmiles-per-gallon rating). As used herein, the term “attribute” refers toany specification that defines a property of an object such as avehicle, and the term “attribute value” or “value” (of an attribute)refers to the specific data populating such an attribute. For example,different cars may be defined using the same attribute (e.g., exteriorcolor) and have different values (e.g., blue) for that attribute. Thesame value (e.g., blue) may exist for different attributes (e.g.,exterior color and interior color).

Certain attributes may be represented as categorical variables. Anattribute represented as a categorical variable may be referred to as acategorical attribute. For example, the attribute of vehicle conditionmay take on values that represent the categories of new vehicles andused vehicles. Other examples of attributes that may be represented ascategorical variables include, but are not limited to: make; model; bodystyle (whose value may represent, for example, convertible, coupe,hatchback, sedan, sports utility vehicle (SUV), truck, minivan, van,and/or wagon); exterior color; interior color; number of doors; enginetype (whose values may represent, for example, 3 cylinders or less, 4cylinders, 5 cylinders, 6 cylinders, 8 cylinders, 10 cylinders or more,and/or electric), transmission type; fuel type (whose values mayrepresent, for example, diesel, electric flexible-fuel, gasoline, andhybrid); drive type; and attributes pertaining to car features (forexample, if the car feature is whether the car has a backup camera orheated seats, the values may be yes or no). The value of categoricalattributes may be stored in any format, including numerical or stringrepresentations. Additionally, the computer system 110, when assemblingthe list of vehicles to be stored in the database 112, may processinformation from the vendors 140 so that an attribute is assigned. Forexample, the computer system 110 may assign a single value representingsilver for different shades of silver used by different vehiclemanufacturers. Categories represented by values of a categoricalattribute may also be referred to as classes.

Certain attributes may be represented as quantitative variables. Anattribute represented as a quantitative variable may be referred to as aquantitative attribute. A quantitative variable may be a continuousquantitative variable or a discrete quantitative variable. Examples ofattributes that may be represented as quantitative variables includemileage, price, year, and fuel economy. A quantitative variable may haveordinal values that can be ordered from low to high. Thus, a group ofvalues of a quantitative variable may be characterized by metrics suchas a mean or median value. A quantitative variable may have any suitablenumber of decimal places, if any, or other manner of discretization. Forexample, the variable of vehicle year may have discretized values suchas 2018, 2019, and 2020, etc.

It is noted that certain attributes may be represented as either aquantitative variable or categorical variable, and the representation ofsuch attributes may depend on the desired implementation. For example,the attribute of year may be represented as either a quantitativevariable or a categorical variable, depending on the desiredimplementation.

FIG. 2 depicts a method for determining one or more vehiclerecommendations based on user interaction, according to one or moreembodiments of this disclosure. A vehicle may be described by variousattributes, as described above, and a user may have preferencesregarding such attributes. The method depicted in FIG. 2 includes amethodology for selecting the order of attributes to ask a user about.

The method depicted in FIG. 2 may be performed by computer system 110.In some examples, the method may be performed as part of auser-interaction service for recommending vehicles, such as a servicethat uses an artificial intelligence agent to interact with a user. Themethod depicted in FIG. 2 may involve a user of any one of the userdevices 120. In the following descriptions, user device 121 is used asan example of the user device.

Step 201 may include determining a candidate set of vehicles. Thecandidate set of vehicles may be a subset of a base set of all vehiclesstored in database 112. In some examples, the candidate set is selectedbased on user geography. For example, the computer system 110 mayaggregate information on vehicles over multiple geographical regionsinto a base set of vehicles. Therefore, in order to provide a vehiclerecommendation for a particular user, the computer system 110 may firstdetermine the location of the user, and select the candidate set ofvehicles as vehicles, selected from the base set, that are located atvenues within a certain geographical distance from the user. Thelocation may be determined based on, for example, a user inputtransmitted from user device 121 of the user to the computer system 110,an internet protocol address of the user device 121, or some otherinformation available to the computer system 110. The geographicaldistance may be input by the user or have a default value adjustable bythe user.

In some implementations, the candidate set of vehicles may be based onuser interactions. For example, the computer system 110 may present aseries of preliminary questions to user device 121 to ask aboutpreferences. The user's response may then be used to select thecandidate set of vehicles in step 201. For example, if the preliminaryquestions asks a user about a desired vehicle make, and the userresponds with a certain vehicle make, then the computer system 110 maynarrow the base set to vehicles, within a certain geographical distancefrom the user, that are of the indicated make.

Step 202 may include selecting a vehicle attribute to query the user,based on a frequency distribution of the vehicle attribute. The vehicleattribute selected in this step is to be the topic of a question for theuser. This step may include determining a set of vehicle attributes fromwhich a particular vehicle attribute is to be selected, determining afrequency distribution of each vehicle attribute in the set of vehicleattributes, and selecting the particular vehicle attribute from the setof vehicle attributes based on the frequency distributions. The set ofvehicle attributes may be determined as the set of vehicle attributesthat are represented in the candidate set of vehicles. In someembodiments, the selection may be based on a computation of entropyvalues for the vehicle attributes in the set of vehicle attributes.

Steps 202 through 205 may be an iterated process that is performedmultiple times, such that in each iteration, a different vehicleattribute is selected in step 202. As will be described below, thecandidate set of vehicles and/or the set of attributes from which aparticular vehicle attribute is to be selected may be adjusted aftereach iteration.

FIG. 3 depicts a method of performing step 202 that involves computationof entropy values for the vehicle attributes in the set of vehicleattributes. Any step that is part of the method depicted in FIG. 3 maybe implemented as a sub-step of step 202 of FIG. 2.

Step 310 may include determining a set of vehicle attributes. In someexamples, the set of vehicle attributes may be a default set of vehicleattributes pre-configured to include all possible vehicle attributesthat a user may be asked about. However, it is also possible toconstruct the set of vehicle attributes based on evaluation of thecandidate set of vehicles. For example, during the first iteration ofsteps 202 through 205, the candidate set of vehicle attributes mayconstructed to include any vehicle attribute that is used tocharacterize vehicles in the candidate set of vehicles. For eachsubsequent iteration, the set of vehicle attributes may be reduced so asto omit the vehicle attribute that was selected in step 202 of theprevious iteration.

Box 320 represents a normalized entropy computation process that may beperformed for each vehicle attribute in the set of vehicle attributes.Different instances of the process, respectively performed for differentvehicle attributes, may be performed sequentially or in parallel. Step321 may include determining a type of attribute for the particularattribute that is being evaluated. Depending on whether the attributebeing evaluated is categorical or quantitative, step 322A or step 322Bmay be performed to calculate a frequency distribution for thatattribute.

The frequency distribution in step 322A or 322B describes the number oftimes a value (or group of values, such as a bin of values) occurs indata points of the candidate set of vehicles, for each value (or eachgroup of values) in a certain set of values (or in a certain set ofgroups of values). The number of occurrences of a value in a data setmay also be referred to as a “frequency” of that value. A frequencydistribution of the values of an attribute may also be referred to as ahistogram of the attribute. In this disclosure, the term histogram isnot limited to histograms for binned quantitative variables.

If the attribute in step 321 is a categorical attribute, then theprocess may proceed to step 322A, which may include determining thenumber of occurrences (also referred to as the “frequency”) of eachvalue of the attribute in the candidate set of vehicles, to determine afrequency distribution for values of the categorical variable.

If the attribute is a quantitative attribute, then process 320 mayproceed to step 322B, which may include grouping the data points of theattribute into bins and determining the number of occurrences (alsoreferred to as the “frequency”) of each bin in the candidate set ofvehicles, to obtain a frequency distribution for the bins. In thiscontext, an occurrence of a bin in the candidate set of vehicles may bethe occurrence of any value within the bin in the candidate set ofvehicles. The grouping of data points into bins may be performed inorder to discretize the different values of the data points of theattribute, for purposes of calculating entropy, as described below. Thenumber of bins and the size of the bins may depend on the circumstancesand/or desired implementation. For example, for a given vehicleattribute subject to step 322B, the number of bins and the size of binsmay be determined automatically based on statistical characteristics ofthe set of data points of the attribute in question (e.g., variance)and/or configured manually. For a given vehicle attribute, the size ofthe bins may be the same for all bins, but it is also possible for thebins to be of differing sizes. As used herein, the term “bin” isinterchangeable with “bucket.”

A bin may include one or more values. If the set of data points of theattribute is sufficiently discretized and/or the number of differentvalues in the set of data points is small, it is possible for the binsto represent single values rather than ranges of values, in which casestep 322B may be performed in a manner similar to step 322A forcategorical attributes. For example, if the attribute of vehicle year isrepresented as a discrete quantitative attribute having only wholenumbers as possible values, then each bin may be a single year,especially if the number of the different values for vehicle year isrelatively small. On the other hand, if the values in the set of datapoints are not highly discretized, then each bin may be a range ofvalues.

In some implementations, the set of data points of one or morequantitative attributes may be subject to outlier processing. Suchprocessing may be performed prior to grouping the data points into binsin step 322B, in order to prevent outlier data points from biasing thedistribution for the subsequent calculation of entropy. In someimplementations, the data points of the attribute in question may becapped so as to not exceed an upper-bound limit and/or capped so as tobe not below a lower-bound limit. The upper-bound limit and/or thelower-bound limit may be determined automatically, based on statisticalcharacteristics of the set of data points of the attribute in question(e.g., upper and lower percentile values), and/or configured manually.The capping operation described above may include replacing values thatare higher than the upper-bound limit and lower than the lower-boundlimit with the respective values of the upper-bound limit and thelower-bound limit. Whether or not an attribute is to be subject tooutlier processing may be determined based on automatic processes (e.g.,outlier detection techniques) or based on manually-configured settingsfor specific attributes.

As an example to illustrate the methodologies described above, theattribute of price may have data points in the candidate set of vehiclesthat are outliers. Such outliers may correspond to vehicles in theinventory of a certain vendor that have an unusually high or unusuallylow price. The computer system performing step 322B may determine thatprice is an attribute that is subject to outlier processing, and maycompute the 5^(th) and 95^(th) percentile values of price in the set ofvehicles. Subsequently, data points having a value of price that isbelow the 5^(th) percentile value may be set to the 5^(th) percentilevalue, and data points having a value of price that is above the 95^(th)percentile value may be set to the 95^(th) percentile value.

Furthermore, after the data points have been grouped into bins, anadditional processing step may be performed, in which bins that aresubstantially empty (e.g., having a low number of occurrences ascompared to other bins) may be removed for purposes of the calculationof entropy.

Step 323 may include determining an estimate of normalized entropy basedon the frequency distribution calculated in step 322A or 322B.

In general, the entropy, S, of a discrete random variable X havingpossible values x₁, . . . , x_(n) may be computed according to thefollowing expression:

${S(X)} = {- {\sum\limits_{i}^{n}{{P\left( x_{i} \right)}\log_{b}{P\left( x_{i} \right)}}}}$P(X) is the probability mass function, such that P(x_(i)) is theprobability for X=x_(i), and “b” is an arbitrary logarithm base (e.g.,b=2, 10, or e). Various examples in this disclosure generally use b=2.However, it is understood that the disclosure is not so limited and thatother values of b may be used without departing from the scope of thisdisclosure.

The normalized entropy, η(X), may be calculated according to any of thedefinitions in the following expression:

${\eta(X)} = {\frac{S(X)}{\log_{b}(n)} = {{- {\sum\limits_{i}^{n}\frac{{P\left( x_{i} \right)}\log_{b}{P\left( x_{i} \right)}}{\log_{b}(n)}}} = {\log_{n}\left( {\prod\limits_{i = 1}^{n}{P\left( x_{i} \right)}^{- {P{(x_{i})}}}} \right)}}}$In information theory, normalized entropy is also known as “efficiency.”In this disclosure, the term “normalized entropy” is generally used torefer to the information theory concept expressed above. Depending oncontext, the term “efficiency” (and similar terms) may have a differentor more generalized meaning. For example, a series of questions may beregarded as having high efficiency if it elicits a high amount ofinformation from the user using a low number of questions.

In applying the above expressions to vehicle attributes, the variable“X” may represent any categorical or quantitative vehicle attribute.P(X) may represent the probability mass function for the user'spreference for the vehicle attribute X. The values x₁, . . . , x_(n) maytherefore correspond to values of a categorical attribute, or the binsof a quantitative variable. Accordingly, the probability P(x_(i)) mayrepresent, for example, the probability that the user wishes to purchaseor shop for vehicles of a certain characteristic represented byattribute value x_(i).

In some implementations, a form of the frequency distribution that isnormalized to sum to 1 may be used as the probability mass functionP(X). In such implementations, the frequency distribution calculated instep 322A or 322B for a particular vehicle attribute may be normalizedfor use in step 323 to obtain a normalized frequency distribution thatis normalized to sum to 1, and such normalized frequency distribution ofthe attribute may be used as a proxy for the probability mass functionfor the user's preference for that attribute. Such a normalizeddistribution may be referred to as a percentage distribution describingthe percentage of each value (or groups of values) among all values (orall groups of values) in the candidate set of vehicles. A normalizedfrequency distribution may also be referred to as a relative frequencydistribution.

In serving as a proxy for the probability mass function for the user'spreference for an attribute, the corresponding normalized frequencydistribution may be treated as an estimate of the probability massfunction for purposes of computing entropy and normalized entropy. Forexample, if the candidate set of vehicles includes 1000 total vehicles,consisting of 300 SUVs, 120 coupes, and 580 sedans, the percentages ofthe attribute of “body style” would be 30% for “SUV”, 12% for “coupe,”and 58% for “sedan.” Accordingly, if these percentages are used asprobabilities for purposes of calculating entropy, then the entropy S(in bits) may be calculated according to the following expression:S=−(0.3)log₂(0.3)−(0.12)log₂(0.12)−(0.58)log₂(0.58)=1.34. Since thereare n=3 total values for the attribute of “body style”, the normalizedentropy is η=S/log₂(3)=0.848. In this example, a normalized frequencydistribution of an attribute of a sample of cars that are available forpurchase is used as an approximation for the probability distribution ofthe customer's desired attributes.

The normalized frequency distribution of an attribute may be used as aproxy for, and an estimate of, the probability mass function based onthe assumption that the amount of inventory for a certain vehiclecharacteristic tends to correlate with consumer needs for that vehiclecharacteristic, and hence the tendency of a user to purchase a vehiclehaving that vehicle characteristic. In this disclosure, the term“estimate of entropy” and the term “estimate of normalized entropy” mayrespectively refer to a value of entropy and a value of normalizedentropy calculated based on an estimate of the probability massfunction. As noted above, a proxy variable, such as a percentagedistribution in the form of the aforementioned normalized frequencydistribution, may serve as an estimate of the probability mass function.

For categorical attributes, the list of n values x₁, . . . , x_(n) mayinclude all values that appear in the candidate set of vehicles for aparticular categorical attribute in question. In some implementations,the list of n values x₁, . . . , x_(n) may further include values thatare possible but that do not appear in the candidate set. Such valuesmay be referred to as empty classes. For example, continuing with theabove example of a candidate set of 1000 total vehicles, the attributeof “body style” may have other possible values besides “SUV”, “Sedan”,and “Coupe”, but none of these other possible values for body styleactually appear in the set of 1000 total vehicles. However, theinclusion of empty classes would increase “n”, so as to increase thenormalization factor log₂(n) in the computation of normalized entropy.Therefore, in some implementations, empty classes may be excluded fromthe list of n values. In such implementations, empty classes would notaffect the value of “n” in the normalization factor log₂(n), sincevalues for empty classes would not be counted among the values of x₁, .. . , x_(n).

FIGS. 4A through 4H are charts that illustrate the normalized frequencydistributions and the corresponding estimates of normalized entropy forvarious vehicle attributes, for a candidate set of vehicles. The titlesof the charts display the name of the attribute (“vehicle condition”,“transmission”, “year”, “exterior color”, “body style”, “price”,“mileage”, and “combined mpg”), and the respectively computed estimateof normalized entropy is displayed next to the attribute name. Thehorizontal bars represent the percentages for each value or bin, whereinthe percentages sum to 1.

Referring to FIGS. 4A and 4B, for example, it is shown that, in thecandidate set of vehicles, the frequency distribution for the attributeof “vehicle condition” is more uniformly distributed than the frequencydistribution for the attribute of “transmission.” Under the assumptionthat the percentage of an attribute value in the candidate set ofvehicles represents the probability of a user in purchasing a vehiclehaving that attribute value, asking a question on vehicle condition(e.g., “What vehicle condition are you looking for?”) may be moreeffective than asking a question on vehicle transmission (e.g., “Whatvehicle transmission are you looking for?”) for purposes of narrowingdown the candidate set of vehicles. In this circumstance, there isrelatively high certainty that a typical user would prefer automatictransmission over manual transmission (as indicated by low entropy),whereas there is relatively low certainty that a typical user wouldprefer either used or new (as indicated by high entropy). Therefore,based on the premise that questions asked should prioritize less-certainchoices over more-certain choices, asking the question on vehiclecondition may be more effective than asking the question on vehicletransmission. Therefore, as will be discussed in more detail below inrelation to step 330, it may be beneficial to select attributes thathave relatively high entropy for purposes of asking a question to auser.

FIGS. 4C to 4H show additional examples of normalized entropy. FIG. 4C,for example, shows the estimated normalized entropy for the attribute ofyear. In general, the vehicle attribute of year may be represented aseither a categorical or quantitative attribute, depending on the desiredimplementation. In the instant example of FIG. 4C, year is representedas a categorical attribute, with possible values of 2006 to 2018 asshown. In the instant example, empty classes corresponding to values of2006, 2007, 2009, and 2008 are included in the count for n, for anormalization factor, log₂(n), of 3.8074 using n=14. If the four emptyclasses are removed from the count of “n,” then the normalization factorwould instead be 3.3219, using n=10, which would correspond to anormalized entropy of 0.6764 rather than 0.5901.

FIGS. 4D and 4E show frequency distributions and corresponding estimatesof normalized entropy for additional categorical attributes, namelyexterior color (FIG. 4D) and body style (FIG. 4E). As in the case ofFIG. 4A, empty classes, such as “turquoise” and “van,” may be removedfor purposes of the normalization factor, if desired. FIGS. 4F to 4Hshow frequency distributions and corresponding estimates of normalizedentropy for quantitative attributes of price, mileage, and combinedmiles-per-gallon. The data of such attributes may be subject to outlierprocessing as described above.

As shown in FIGS. 4A to 4H, the use of normalized entropy permitsdifferent attributes to be compared with one another, even if theydiffer in the number of values or bins.

Returning to FIG. 3, step 330 may include selecting, from the set ofvehicle attributes, a vehicle attribute based on the values ofnormalized entropy. In some examples, the selection may be based on thevalues of normalized entropy alone. In such examples, step 330 mayselect the vehicle attribute whose respectively calculated normalizedentropy is highest among all vehicle attributes in the set of vehicleattributes. For example, among the attributes shown in FIGS. 4A to 4H,the attribute of “vehicle condition” has the highest estimate ofnormalized entropy (0.9855). Therefore, if the selection methodology isbased on the highest estimate of normalized entropy, then the attributeof “vehicle condition” would be selected over the other attributes shownin FIGS. 4A to 4H for the illustrated set of vehicle attributes.

In some examples, step 330 may select a vehicle attribute based on acombination of factors that includes the value of normalized entropyalong with one or more additional factors, such as weights,randomization factors, and/or other factors. For example, the computersystem performing step 330 may calculate an index as a function of theestimate of normalized entropy and the one or more additional factors,and select the attribute based on the order of the values, among thedifferent attributes, for the index. The one or more additional factorsmay include tiebreaker factors, such as a default order when theaforementioned index is the same for different attributes. The one ormore additional factors affecting selection may be the same or differentfor different iterations of steps 202 through 205 of FIG. 2.

For example, the respective normalized entropy values of the vehicleattributes in the candidate set of vehicle attributes may be multipliedor offset by respective weights. Such weights may be set so as torepresent relative preferences for certain attributes over others, or tootherwise make adjustments to the calculated normalized entropy. Forexample, attributes that are favored for selection may have theirnormalized entropy increased by 0.1 or 10%, while attributes that arenot favored for selection may have their normalized entropy reduced by0.1 or 10%. Randomization factors, such as an increase or decrease ofthe normalized entropy by a random value or percentage, may beimplemented in the manner of weights.

By use of additional factors such as weights, an attribute with acalculated normalized entropy that is slightly lower than that ofanother attribute may potentially still be selected over that otherattribute. However, even if such additional factors are used in step330, the selection process may be implemented so as to be primarilybased on the normalized entropy calculated in step 323, so thatattributes with higher normalized entropy tend to be selected overattributes with lower normalized entropy.

Referring again to FIG. 2, step 203 may include transmitting, to a userdevice operated by the user, an inquiry for user preference regardingthe selected vehicle attribute. The inquiry may be transmitted by thecomputer system 110 to user device 121 over network 130 and may serve asrequest for the user preference. In some examples, the inquiry mayinclude or otherwise indicate an inquiry message requesting the user tosupply the user preference. The inquiry message may be presented to theuser through user device 121, and may be presented in a natural languageformat.

The inquiry message may be a question asking for a response indicativeof user preference regarding the selected vehicle attribute (e.g., “whatvehicle condition are you looking for?” or “what body style are youlooking for?”, “indicate the vehicle condition you are looking for” or“please select the vehicle condition you are looking for”). Such aquestion may be in any suitable form. For example, the question may havean interrogative grammatical structure (e.g., “what vehicle conditionare you looking for?”) or a non-interrogative grammatical structure(e.g., “indicate the vehicle condition you are looking for”).

The inquiry and inquiry message may, but do not necessarily need to,recite the selected vehicle attribute. In some implementations, or forsome vehicle attributes, the inquiry message may ask about a subjectmatter that is related to the selected vehicle attribute such that ananswer to the inquiry message may still indicate a user preference forcertain values of the selected vehicle attribute. For example, if theselected vehicle attribute is “price,” then the inquiry message may askabout a desired monthly payment (e.g., a desired monthly payment for carfinancing) instead of price. In this example, a response to the inquirymessage that is indicative of a certain monthly payment may indicate auser preference for a certain price, based on a correspondence betweenthe monthly payment and price, and based further on default oruser-specified values for loan parameters (e.g., term length andinterest rate).

The inquiry message may be a general question for the selected vehicleattribute without directly asking about specific values of the selectedvehicle attribute. That is, question may be in the form of “what vehiclecondition are you looking for?” or “select a vehicle condition” thatpertains to the selected vehicle attribute in general. A questionphrased in such a manner permits the response from the user to indicatea selection of one or more values of the attribute. By contrast, aquestion that pertains to specific values of the attribute, such as“would you like a coupe?” or “blue exterior color?” or “newer than2007?” would suggest a binary (e.g., “yes” or “no”) answer regardingspecific value(s) of the selected vehicle attribute. By asking a generalquestion, the content of the user's response may be flexible, and notlimited to certain formats such as a binary, yes-or-no response.

To present the inquiry message to the user, the computer system 110 maycause the inquiry to be presented through a user interface, such as agraphical user interface displayed on the user device 121 operated bythe user, and/or a voice user interface implemented by the user device121. For example, the computer system 110 may transmit a message to theuser device 121 controlling, or otherwise causing, the user device 121to present the inquiry in response to receive the message. The messagemay include the inquiry to be presented (e.g., visually displayed and/oraudibly presented).

In some examples, the user interface by which the inquiry is presentedmay be a graphical user interface displayed on a web browser or otherapplication executed on the user device 121. If the user device 121 is amobile computer device, the graphical user interface may be provided bya mobile app executed thereon. In some examples, the user device 121 mayinclude a voice assistant, and the user interface may be broadcast tothe user in audio form using the voice assistant.

Examples of a graphical user interface are provided in FIGS. 5A and 5B,which are discussed in greater detail below. However, in general, instep 205, the computer system 110 may present the inquiry to the user ofthe user device 121 via any suitable method, involving any suitable userinterface.

Step 204 may include receiving, from the user device, a response to theinquiry. The response may indicate the user preference regarding theselected vehicle characteristic. In order for the computer system 110 toreceive the response, the user may, for example, input the responseusing a user interface (e.g., graphical user interface or a voice userinterface) provided at the user device 121, which may be the same userinterface discussed above in relation to step 203, or a separate userinterface.

To generate the response that is received in step 204, the user mayinput certain value(s) of the selected attribute, to indicate that thosevalue(s) are desired by the user. The client device 121, upon receivingan input from the user indicating the desired value(s), may transmit theresponse to the computer system 110 over network 130 indicating thedesired value(s) indicated by the user input.

As noted above, the inquiry message may be a question for soliciting aresponse that identifies one or more value(s) desired by the user.Therefore, the user interface may permit the user to input variousvalues or combinations of values to be indicated as desired. Forexample, in implementations in which a graphical user interface isutilized by the user to generate the response, the graphical userinterface may include interactive graphical user interface elements(e.g., buttons, drop down lists, selection menus, text inputs, sliderbars, and/or numerical value selectors) through which the user mayselect one or more desired value(s), and input the selection. Examplesof such implementations are illustrated in FIGS. 5A and 5B, discussedbelow.

The user interface may permit the user to make a selection from amongthe values used in step 322A in the case of categorical attributes, orthe bins used in step 322B in the case of quantitative variables, to beindicated in the response transmitted by the client device 121 as beingdesired. However, there is no requirement for the desired value(s)selected by the user to match the values (or bins) in step 322A or 332B.For example, the user interface may permit the user to input desiredvalue(s) that represent certain values of the selected vehicle attribute(e.g., a value of monthly payment that represents values of price), ordesired value(s) that are different from the bins used in step 322B. Thevalues and bins in steps 322A and 322B are used for selection of thevehicle attribute for purposes of generating the inquiry, and do notnecessarily need to be used for narrowing down the candidate set ofvehicles (e.g., as in step 205, described below).

For quantitative attributes, the user interface may permit a selectionof ranges, including open ended ranges. For example, if price is theselected attribute, the user may enter a price range between two values,or a price range that is greater than or less than a certain value.

In some examples, the response that is received in step 204 may indicatethe absence of a user preference regarding the selected vehicleattribute. For example, the user interface may permit the user to entera “skip” command, which may indicate to the computer system 110 that thevehicle attribute is to be skipped. Examples of user interfacespermitting entry of a “skip” command are shown in FIGS. 5A and 5B.

Step 205 may include adjusting the candidate set of vehicles based onthe response, if the response indicates a user preference for only aportion of vehicles in the candidate set. Step 205 may includedetermining whether the response indicates the exclusion of vehicleshaving one or more values of the selected attribute from furtherconsideration, and, upon determining that the response does indicate theexclusion of vehicles having one or more values of the selectedattribute from further consideration, adjusting the candidate set ofvehicles to exclude vehicles having those one or more values of theselected attribute.

In some examples, the response may indicate that certain value(s) of theselected attribute are desired by the user. Based on such a response,the computer system 110 may determine that vehicles of the candidate sethaving any value of the selected attribute other than the aforementioneddesired value(s) are to be excluded. That is, an indication of certainvalues of an attribute being desired may, in various implementations, betreated as a user command for exclusion of vehicles having all othervalues of the attribute.

If the response indicates a “skip” command, then the candidate set ofvehicles may be maintained without exclusion of any vehicles therefrom.

Step 206 may include determining whether another iteration of steps 202to 205 is to be performed. This determination may be based on one ormore of factors. For example, the computer system 110 may reiterate theprocess (206: YES) until a termination condition has been satisfied. Thetermination condition may be, for example, the completion of apredetermined number of total iterations, a quantity of vehiclesremaining in the candidate set (after any exclusions made in accordancewith the response(s) have been applied), a percentage of vehiclesremaining in the candidate set relative to the initial candidate set asdetermined in step 201 (after any exclusions made in accordance with theresponse(s) have been applied), the values of the estimates ofnormalized entropy for the remaining vehicle attributes (e.g., whetherany remaining vehicle attribute has an estimated normalized entropysatisfying a certain threshold), or a combination of conditionsincluding at least one of the foregoing.

As noted above, if the process is reiterated, the vehicle attribute thatis selected in step 202 may be excluded from selection in the same stepin each subsequent iteration, such that step 202 results in theselection of a different vehicle attribute in every subsequentiteration. Furthermore, it is also possible for a subsequent iterationto terminate prior to step 206. For example, step 202 may includeconditions for skipping to step 207 without performance of steps 203through 206.

Step 207 may include presenting a recommendation of one or morevehicles. The recommendation may indicate or represent the candidate setas reduced in the prior steps or one or more vehicles from the candidateset, and may be presented to the user device 121. In some examples, alist of one or more recommended vehicles or a portion of the list may betransmitted to the user device 121 for display on the user device 121(see FIG. 6). In other examples, the recommendation may indicate orrepresent the list of vehicles by being a hyperlink to a webpage thatincludes the list of one or more recommended vehicles, or a summaryrepresentation of the one or more recommended vehicles (such as a listof each unique combination of make and model represented in the one ormore recommended vehicles).

While the methods of FIG. 2-3 have been discussed with reference toembodiments in which computer system 110 and user device 121 areseparate from one another (e.g., remotely connected through network130), it is also possible the computer system 110 and the user device121 to be the same computer system or the same computing device, inwhich case any information between computer system 110 and user device121 that may take place over network 130 may be communicated betweenapplications running on the same computer system or computing device.Furthermore, it is understood that the methods of FIGS. 2-3 areapplicable to products other than vehicles.

FIGS. 5A, 5B, and 6 illustrate examples of user interactions with anartificial-intelligence interactive agent. The interactive agent may bea chatbot provided by a service of the computer system 110. The chatbotmay have the ability to perform the method of FIGS. 2 and 3, among otherfunctionalities. The chatbot may be programmed to interact with a userto help the user select find a vehicle to purchase or lease.

The respective screens 500A and 500B of FIGS. 5A and 5B may be displayedon a client application executed by a user device (e.g., user device121). The client application may be a web browser, a web browserextension, a text-messaging application (e.g., SMS messagingapplication), or an application (e.g., mobile app) designed to interfacewith the chatbot. The client application may have a user interfaceenabling the user to interact with the chatbot by inputting textqueries.

The user or the chatbot may initiate a conversation session. In FIG. 5A,messages 510, 511, 512, 513, 521, and 522 may be communicated as part ofan interactive session between the chatbot and the user. For example, atthe start of the session, the chatbot may present a message 510 thatpresents information about the functionality of the chatbot.

The chatbot may then output message 511, which may include a question(“What vehicle condition are you looking for?”). To generate message511, the chatbot may perform the method illustrated in FIG. 2, to selectthe attribute of vehicle condition pursuant to step 202. That is, in theexample interaction shown in FIG. 5A, the attribute of vehicle conditionmay be determined, according to step 202 and the method shown in FIG. 3,to be the optimal vehicle inquiry to ask the user about. For example,the attribute of vehicle condition may have the highest estimate ofnormalized entropy among a plurality of vehicle attributes that may beselected. After selecting the attribute of vehicle condition, thechatbot may cause the computer system 110 to transmit an inquiry to theuser device 121, so as to cause the user device 121 to output themessage 511 on a graphical user interface.

As shown, the message 511 may include a selection interface 511Aincluding interface elements permitting the user to select a response.Such interface elements may include a corresponding interface element(e.g., intractable button) for each value of the attribute of vehiclecondition represented in the candidate set of vehicles. In this example,represented values are the values of “new” and “used.” The selectioninterface 511A may include a “skip” interface element, permitting theuser to indicate that he or she has no preference, and a “next”interface element, which permits the user to enter the selected input.For example, the user may select one or both of “new” and “used” andpress the “next” interface element to submit the selection. In theinstantly illustrated example, the user has selected the value of “new,”as indicated by response 521.

As described above in relation to step 204 of FIG. 2, the user device121 may receive a user input (indicating submitting the selection of“new,” in this example) through the user interface, and transmit aninquiry response to the computer system 110 indicating the user input.Upon receiving the user input (step 204), the chatbot may perform step205. In performing step 205, the chatbot may eliminate the vehicles inthe candidate set of vehicles that do not have the value of “new” forthe attribute of “vehicle condition,” and determine that anotheriteration should be performed (step 206: YES).

In the second iteration, the chatbot may determine that, out of theremaining vehicle attributes other than the attribute of vehiclecondition, the attribute of body style is the most optimal attribute toask the user about. For example, the chatbot may determine that, basedon the data set of vehicles in the candidate set of vehicles that havethe value of “new” for the attribute of vehicle condition, the attributeof body style has the highest estimated of normalized entropy. As shownin FIG. 5A, the chatbot may cause the client device 121 to displaymessage 512 including the question of “what body style are you lookingfor?”.

Message 512 may include a selection interface 512A listing the differentvalues for the attribute of body style, and permitting the user toselect one or more of such value. In the instant example, as shown inmessage 522 in FIG. 5B, the user selects “sedan” and “SUV.” Accordingly,the chatbot may once again adjust the candidate set of vehicles, inaccordance with step 205 of FIG. 2, so as to exclude vehicles that donot have “sedan” or “SUV” for the attribute of body style, and proceedto a third iteration.

In the third iteration, the chatbot presents a message 513 with thequestion of “what is your preferred monthly payment.” The message may begenerated upon selection of price as the vehicle attribute to ask theuser about. As described above in relation to step 203, the inquiry maypertain to a topic that is related to the selected vehicle attribute. Inthis example, the inquiry pertains to monthly payment, which may berelated to the attribute of vehicle price based on a correspondencebetween monthly payment and price. The selection interface 513B mayinclude a slider permitting a user to select a value within a range, andsubmit the selected value. For quantitative attributes, the selection ofa particular value may be interpreted as a selection of a bin or arange. For example, a particular value of monthly payment may correspondto a particular value of price, which may correspond to a bin or rangeof values.

FIG. 6 illustrates an example of a vehicle listing interface 600displaying a representation of a recommendation of one or more vehicles.The vehicle listing interface 600 may be displayed on a user device(e.g., user device 121) upon determining one or more recommendedvehicles in according with step 207 of FIG. 2. The computer system 110may transition the user from the interface shown in FIGS. 5A and 5B tothe vehicle listing interface 600, or otherwise direct the user to thevehicle listing interface 600 by, for example, a hyperlink. In someexamples, the user interface 600 may be a web page transmitted by thecomputer system 110 over network 130 for display on user device 121. Thevehicle listing interface 600 may include a list of vehicles 620. Thelist of vehicles may be selected as vehicles that satisfy one or morecharacteristics specified on selection panel 640. Selection of vehiclecharacteristics from the selection panel 640 may be further indicated asfilters 610.

According to some embodiments, upon completion of step 207 of FIG. 2,the computer system 110 may transmit the vehicle listing interface 600(as, for example, a web page) to user device 121 such that the filters610 are pre-set in accordance with the response(s) received from theuser during the method of FIG. 2. That is, the user preferencesindicated during the method of FIG. 2 may be converted into searchfilters of the vehicle listing interface 600, and the resulting list ofvehicles 620 would include the one or more vehicle recommendationsresulting from the method of FIG. 2. In the illustrated example, theinteractions shown in FIGS. 5A and 5B may result in the filters 610 of“new”, “$28 k-$35 k”, “sedan”, and “SUV” being applied, as shown in FIG.6, filtering the list of vehicles to new vehicles that are either sedansor SUVs and have a price in the range of $28 k-$35 k. The range of $28k-$35 k may be a bin of values used in step 322B, or a range that ischosen based on the response received in step 204.

The methodologies described in this disclosure, including themethodologies for selecting a vehicle attribute for inquiry, permit anefficient series of questions to be generated for purposes of learning auser's preferences as to various product attributes. In particular, byanalyzing the distributional characteristics of the attribute or derivedmeasures such as normalized entropy, it becomes possible to direct thequestions to particular vehicle attributes for which there is lowcertainty as to the user's preferences. Therefore, it is possible torealize an efficient user-question process that obtains a high amount ofinformation using a low number of questions.

The methodologies for selecting the vehicle attribute for inquiry arecompatible with different manners of the inquiry and different forms ofresponses to the inquiry. For example, the inquiry may elicit a binary(e.g., “yes or no”) response, a response indicating a selection ofmultiple possible values of an attribute, or a response indicating nopreference. Since the methodologies for selecting the vehicle attributefor inquiry does not rely on a particular form of inquiry or response tothe inquiry, the methodologies may be applied to different manner ofuser interactions.

Furthermore, the methodologies described in this disclosure may beincorporated into an interactive agent such as a chatbot, therebyimproving the effectiveness of the chatbot in providing productrecommendations.

In general, any process discussed in this disclosure that is understoodto be computer-implementable, such as the processes shown in FIGS. 2-3,may be performed by one or more processors of a computer system, such ascomputer system 110 as described above. A process or process stepperformed by one or more processors may also be referred to as anoperation. The one or more processors may be configured to perform suchprocesses by having access to instructions (e.g., software orcomputer-readable code) that, when executed by the one or moreprocessors, cause the one or more processors to perform the processes.The instructions may be stored in a memory of the computer system. Aprocessor may be a central processing unit (CPU), a graphics processingunit (GPU), or another type of processing unit.

A computer system, such as computer system 110 or user devices 120, mayinclude one or more computing devices. If the one or more processors ofthe computer system is implemented as a plurality of processors, theplurality of processors may be included in a single computing device ordistribute among a plurality of computing devices. If a computer systemcomprises a plurality of computing devices, the memory of the computersystem may include the respective memory of each computing device of theplurality of computing devices.

FIG. 7 illustrates an example of a computing device 700 of a computersystem. The computing device 700 may include processor(s) 710 (e.g.,CPU, GPU, or other processing unit), a memory 720, and communicationinterface(s) 740 (e.g., a network interface) to communicate with otherdevices. Memory 720 may include volatile memory, such as RAM, and/ornon-volatile memory, such as ROM and storage media. Examples of storagemedia include solid-state storage media (e.g., solid state drives and/orremovable flash memory), optical storage media (e.g., optical discs),and/or magnetic storage media (e.g., hard disk drives). Theaforementioned instructions (e.g., software or computer-readable code)may be stored in any volatile and/or non-volatile memory component ofmemory 720. The computing device 700 may, in some embodiments, furtherinclude input device(s) 750 (e.g., a keyboard, mouse, or touchscreen)and output device(s) 760 (e.g., a display, printer). The aforementionedelements of the computing device 700 may be connected to one anotherthrough a bus 730, which represents one or more busses. In someembodiments, the processor(s) 710 of the computing device 700 includesboth a CPU and a GPU.

Instructions executable by one or more processors may be stored on anon-transitory computer-readable medium. Therefore, whenever acomputer-implemented method is described in this disclosure, thisdisclosure shall also be understood as describing a non-transitorycomputer-readable medium storing instructions that, when executed by oneor more processors, cause the one or more processors to perform thecomputer-implemented method. Examples of non-transitorycomputer-readable medium include RAM, ROM, solid-state storage media(e.g., solid state drives), optical storage media (e.g., optical discs),and magnetic storage media (e.g., hard disk drives). A non-transitorycomputer-readable medium may be part of the memory of a computer systemor separate from any computer system.

It should be appreciated that in the above description of exemplaryembodiments, various features are sometimes grouped together in a singleembodiment, figure, or description thereof for the purpose ofstreamlining the disclosure and aiding in the understanding of one ormore of the various inventive aspects. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaimed invention requires more features than are expressly recited ineach claim. Rather, as the following claims reflect, inventive aspectslie in less than all features of a single foregoing disclosedembodiment. Thus, the claims following the Detailed Description arehereby expressly incorporated into this Detailed Description, with eachclaim standing on its own as a separate embodiment of this disclosure.

Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe disclosure, and form different embodiments, as would be understoodby those skilled in the art. For example, in the following claims, anyof the claimed embodiments can be used in any combination.

Thus, while certain embodiments have been described, those skilled inthe art will recognize that other and further modifications may be madethereto without departing from the spirit of the disclosure, and it isintended to claim all such changes and modifications as falling withinthe scope of the disclosure. For example, functionality may be added ordeleted from the block diagrams and operations may be interchanged amongfunctional blocks. Steps may be added or deleted to methods describedwithin the scope of the present disclosure.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other implementations, which fallwithin the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description. While variousimplementations of the disclosure have been described, it will beapparent to those of ordinary skill in the art that many moreimplementations and implementations are possible within the scope of thedisclosure. Accordingly, the disclosure is not to be restricted.

What is claimed is:
 1. A computer-implemented method for determiningrecommendations based on user interaction, the computer-implementedmethod comprising: determining respective distributional characteristicsof a plurality of vehicle attributes, each of the plurality of vehicleattributes occurring in a set of vehicles; selecting a vehicle attributefrom the plurality of vehicle attributes based on the distributionalcharacteristics of the plurality of vehicle attributes; transmitting, toa user device, an inquiry for user preference regarding the selectedvehicle attribute; receiving, from the user device, a responseindicating the user preference; and determining a recommendation of oneor more vehicles determined based on the received response.
 2. Thecomputer-implemented method of claim claim 1, wherein the selecting thevehicle attribute includes: determining respective estimates ofnormalized entropy of the plurality of vehicle attributes based on therespective distributional characteristics; and selecting the vehicleattribute from the plurality of vehicle attributes based on therespective estimates of normalized entropy.
 3. The computer-implementedmethod of claim claim 2, wherein the respective distributionalcharacteristics are percentage distributions, and in the determining therespective estimates of normalized entropy, the respectivedistributional characteristics are used as respective probability massfunctions of the plurality of vehicle attributes.
 4. Thecomputer-implemented method of claim claim 2, wherein the selecting thevehicle attribute from the plurality of vehicle attributes selects avehicle attribute having a highest estimate of normalized entropy out ofthe respective estimates of normalized entropy of the plurality ofvehicle attributes.
 5. The computer-implemented method of claim claim 2,wherein the plurality of vehicle attributes includes a categoricalattribute, and for the categorical attribute, the respective estimate ofnormalized entropy is normalized based on a total number of values ofthe categorical attribute that appear at least once in the set ofvehicles.
 6. The computer-implemented method of claim claim 1, whereinthe plurality of vehicle attributes includes a quantitative attribute,and the determining the respective distributional characteristicsincludes determining a frequency distribution of the quantitativeattribute by grouping values of the quantitative attribute into bins,each of the values of the quantitative attribute corresponding to arespective vehicle in the set of vehicles, and determining a respectivefrequency of each of the bins, to obtain the frequency distribution ofthe quantitative attribute.
 7. The computer-implemented method of claimclaim 6, wherein the determining the frequency distribution of thequantitative attribute includes: prior to the grouping the values of thequantitative attribute into bins, processing the values of thequantitative attribute by capping one or more outlier values.
 8. Thecomputer-implemented method of claim claim 1, wherein the userpreference indicates a preference for one or more values of the selectedvehicle attribute, the method further includes excluding, from the setof vehicles, vehicles having none of the one or more values for theselected vehicle attribute, to obtain a reduced set of vehicles, and therecommendation includes one or more vehicles of the reduced set ofvehicles.
 9. The computer-implemented method of claim claim 1, whereinthe plurality of vehicle attributes include one or more of: body style,exterior color, interior color, number of doors, vehicle condition,engine types, transmission type, fuel type, mileage, price, year, orfuel economy.
 10. The computer-implemented method of claim claim 1,wherein the selected vehicle attribute is a first vehicle attribute, theuser preference indicates a preference for one or more values of theselected vehicle attribute, and the method further includes: excluding,from the set of vehicles, vehicles having none of the one or more valuesfor the selected vehicle attribute, to obtain a reduced set of vehicles;determining second respective distributional characteristics of a secondplurality of vehicle attributes, the second distributionalcharacteristics being determined based on occurrences of values of theplurality of vehicle attributes in the reduced set of vehicles, thesecond plurality of vehicle attributes each being a vehicle attributeother the first vehicle attribute; selecting a second vehicle attributefrom the second plurality of vehicle attributes based on the seconddistributional characteristics of the plurality of vehicle attributes;presenting, to the user device, a second inquiry for user preferenceregarding the second vehicle attribute, and receiving, from the userdevice, a second response indicating the user preference regarding thesecond vehicle attribute, and the recommendation of one or more vehiclesis determined based further on the second response.
 11. Thecomputer-implemented method of claim claim 1, further comprising:initiating a chat bot session enabling a user of the user device tocommunicate with a chat bot executed by a computer system, wherein theinquiry is transmitted during the chat bot session, and the response isreceived during the chat bot session.
 12. The computer-implementedmethod of claim claim 1, further comprising: presenting, to the userdevice, the determined recommendation of one or more vehicles and one ormore search filters of a vehicle listing interface, the one or moresearch filters having been determined in accordance with the userpreference.
 13. A computer system for determining recommendations basedon user interaction, the computer system comprising: a memory storinginstructions; and one or more processors configured to execute theinstructions to perform a process, the process including: determiningrespective distributional characteristics of a plurality of vehicleattributes, each of the plurality of vehicle attributes occurring in aset of vehicles; selecting a vehicle attribute from the plurality ofvehicle attributes based on the distributional characteristics of theplurality of vehicle attributes; transmitting, to a user device, aninquiry for user preference regarding the selected vehicle attribute;receiving, from the user device, a response indicating the userpreference; and determining a recommendation of one or more vehiclesdetermined based on the received response.
 14. The computer system ofclaim 13, wherein the process further includes, to select the vehicleattribute: determining respective estimates of normalized entropy of theplurality of vehicle attributes based on the respective distributionalcharacteristics; and selecting the vehicle attribute from the pluralityof vehicle attributes based on the respective estimates of normalizedentropy.
 15. The computer system of claim 14, wherein the respectivedistributional characteristics are percentage distributions, and theprocess further includes, to determine the respective estimates ofnormalized entropy, the respective distributional characteristics areused as respective probability mass functions of the plurality ofvehicle attributes.
 16. The computer system of claim 14, wherein theprocess further includes, to select the vehicle attribute from theplurality of vehicle attributes, selecting a vehicle attribute having ahighest estimate of normalized entropy out of the respective estimatesof normalized entropy of the plurality of vehicle attributes.
 17. Thecomputer system of claim 14, wherein the plurality of vehicle attributesincludes a categorical attribute, and for the categorical attribute, therespective estimate of normalized entropy is normalized based on a totalnumber of values of the categorical attribute that appear at least oncein the set of vehicles.
 18. The computer system of claim 14, wherein theplurality of vehicle attributes includes a quantitative attribute, andthe process further includes, to determine the respective estimates ofnormalized entropy, determining a frequency distribution of thequantitative attribute by grouping values of the quantitative attributeinto bins, each of the values of the quantitative attributecorresponding to a respective vehicle in the set of vehicles, anddetermining a respective frequency of each of the bins, to obtain thefrequency distribution of the quantitative attribute.
 19. The computersystem of claim 18, wherein the process further includes, to determinethe frequency distribution of the quantitative attribute: prior to thegrouping the values of the quantitative attribute, processing the valuesof the quantitative attribute by capping one or more outlier values. 20.A computer system for implementing an artificial-intelligenceinteractive agent for determining recommendations based on userinteraction, the computer system comprising: a memory storinginstructions; and one or more processors configured to execute theinstructions to perform a process, the process including: determiningrespective distributional characteristics of a plurality of vehicleattributes describing vehicles in a set of vehicles, each of theplurality of vehicle attributes occurring in the set of vehicles;determining respective estimates of normalized entropy of the pluralityof vehicle attributes based on the respective distributionalcharacteristics; selecting a vehicle attribute from the plurality ofvehicle attributes based on the respective estimates of normalizedentropy; transmitting, to a user device, an inquiry for user preferenceregarding the selected vehicle attribute; receiving, from the userdevice, a response indicating the user preference; and transmitting, tothe user device, a web page including a list of one or more vehiclesfrom the set of vehicles, the list having filters set in accordance withthe user preference.